Impact Of Anomalies Research Articles

Nalar Atensi Pemilihan Perguruan Tinggi Islam di Program Studi FTIK UIN K.H Achmad Shiddiq Jember

The distinction between public and religious campuses often occurs in the community, the dividing barrier between the two has implications for the favorite attention of the nation's generation in choosing universities, and campuses based on religion are always superior. However, in the traces of developments at the State Islamic University KH. Achmad Siddiq has made significant progress every year, and the increasing interest of prospective students is forced to reject many applicants. It is the main attraction of this research for Then answer how the progress occurred. This research is a type of qualitative research, as a bridge so that the results are more in-depth the author will use integrative theory and approach the problem above with a psycho-sociological approach. The focuses that will be studied include; How is the pre-attention anomaly of new students in the Faculty of Tarbiyah and Teacher Training at UIN Jember? What is the process of reasoning? And the new paradigm of attention is developing? The results showed that the attention paradigm that emerged was the reasoning that was built after the anomaly and crisis in the selection of Islamic universities, there were three adjustment processes, namely economical commensurability, formal education commensurability, and moral commensurability. All three are carried out using a negotiation process of general expectations and the impact of anomalies which are believed to be a necessity of the era.

Climate model experiments on the 4.2 ka event: The impact of tropical sea-surface temperature anomalies and desertification

The 4.2 ka event is one of the most prominent climate events of the Holocene. In this study, several climate model experiments were performed to investigate its causal mechanism. The focus in these experiments was on the impact of anomalies in sea surface temperatures (SSTs) associated with enhanced El Nino activity, as this has been proposed as an important driver for the event. Six different SST anomaly scenarios were considered, covering the tropical sectors of the Pacific, Indian, and Atlantic Oceans. In addition, the possible impact of desertification in Northern Africa and Arabia was taken into account. The model results were evaluated against the global humidity anomaly signature for the 4.2 ka event as provided by proxy-based reconstructions from 129 different sites. It is found that a scenario with desertification and warm Pacific SSTs and cold Atlantic SSTs provides the best match with these proxies. This experiment produces significant decreases in precipitation in South Asia, West and East Africa, and increases in South America and northwest North America. These results are partly forced by the strong increase in albedo in North Africa and Arabia, leading to regional cooling and more stable atmospheric conditions, and partly by enhanced atmospheric moisture transport to South America related to an enhanced land-sea thermal contrast. Based on these results, it is proposed that the 4.2 ka event was caused by tropical SST anomalies, leading to drying in North Africa, assisted by ongoing desertification in Northern Africa and Arabia.

An Ensembled Anomaly Detector for Wafer Fault Detection

The production process of a wafer in the semiconductor industry consists of several phases such as a diffusion and associated defectivity test, parametric test, electrical wafer sort test, assembly and associated defectivity tests, final test, and burn-in. Among these, the fault detection phase is critical to maintain the low number and the impact of anomalies that eventually result in a yield loss. The understanding and discovery of the causes of yield detractors is a complex procedure of root-cause analysis. Many parameters are tracked for fault detection, including pressure, voltage, power, or valve status. In the majority of the cases, a fault is due to a combination of two or more parameters, whose values apparently stay within the designed and checked control limits. In this work, we propose an ensembled anomaly detector which combines together univariate and multivariate analyses of the fault detection tracked parameters. The ensemble is based on three proposed and compared balancing strategies. The experimental phase is conducted on two real datasets that have been gathered in the semiconductor industry and made publicly available. The experimental validation, also conducted to compare our proposal with other traditional anomaly detection techniques, is promising in detecting anomalies retaining high recall with a low number of false alarms.

IMPACT ANALYSIS OF ACCIDENTS ON THE TRAFFIC FLOW BASED ON MASSIVE FLOATING CAR DATA

Abstract. The wide usage of GPS-equipped devices enables the mass recording of vehicle movement trajectories describing the movement behavior of the traffic participants. An important aspect of the road traffic is the impact of anomalies, like accidents, on traffic flow. Accidents are especially important as they contribute to the the aspects of safety and also influence travel time estimations. In this paper, the impact of accidents is determined based on a massive GPS trajectory and accident dataset. Due to the missing precise date of the accidents in the data set used, first, the date of the accident is estimated based on the speed profile at the accident time. Further, the temporal impact of the accident is estimated using the speed profile of the whole day. The approach is applied in an experiment on a one month subset of the datasets. The results show that more than 72% of the accident dates are identified and the impact on the temporal dimension is approximated. Moreover, it can be seen that accidents during the rush hours and on high frequency road types (e.g. motorways, trunks or primaries) have an increasing effect on the impact duration on the traffic flow.

Anomalies of the control principle as part of the consolidated financial reporting concept

Subject. The article discusses the impact of the control principle on the existing consolidated financial reporting concept. Objectives. I evaluate possible alterations in the consolidation methodology as a result of the anomaly of the control principle. Methods. The study is based on the method of analogy for scientific hypothesizing. The study methodologically relies upon Thomas Kuhn's paradigm shift theory and the impact of anomalies on methodological principles of normal science. The study combines the analysis and synthesis, induction and deduction, and the method of comparison when analyzing the existing control criteria and identifying anomalies of the control principles as part of the consolidated financial reporting concept, and examining how the anomaly influences the consolidation methodology. Results. I discovered that the control principle in the consolidated financial reporting concept influences the consolidation methodology. I suggest using my own methodological approach to preparing consolidated financial statements in case of the non-equity control the parent company holds over its subsidiary. Conclusions and Relevance. The control principle in the consolidated financial reporting concept has not been formalized, thus causing anomalies affecting methodological principles of consolidated financial reporting. The non-equity control of the parent company over its subsidiary is a case in point. This control induces unavoidable changes in the consolidation methodology. The findings can be used to prepare consolidated financial statements in case of the non-equity control of the parent company over its subsidiary.

Снижение влияния неопределенности исходной информации в интеллектуальных автоматизированных системах управления тепловыми процессами при обеспечении надежной эксплуатации энергетического оборудования

The uncertainty of the source information is used to solve key tasks in an intelligent automated thermal process control system affects the calculation of control actions, the implementation of equipment optimal operating modes and, as a result, leads to degraded reliability. As a rule, this type of information can be qualitative (the use of expert knowledge) or quantitative in nature. In this regard, it is extremely important to reduce the impact of uncertainty. The aim of the study is to identify the types and origins of uncertainty in the source information used by an intelligent automated process control system and to develop approaches to reduce its impact on the reliability of power equipment operation. The approaches used to ensure the specified indicators of reliability, efficiency and environmental friendliness in modern intelligent automated process control systems are based on predictive strategies, according to which the technical condition of equipment and specific degradation processes are predicted. This means that various types of uncertainty can have a significant negative impact. To reduce the influence of uncertainty of the initial information that affects the reliability of power equipment operation, the use of artificial neural networks is proposed. Their application opens the possibility to predict the occurrence of equipment defects and failures based on retrospective data for specified forecast time intervals. A method for reducing the impact of anomalies contained in the source information used in an intelligent process control system for energy facilities is demonstrated. Data omissions and outliers are investigated, the elimination of which reduces the impact of uncertainty and improves the quality of solving key problems in intelligent automated process control systems. Experimental studies were carried out that made it possible to identify the mathematical methods for removing omissions and anomalies in the source information in the best way. Methodological aspects of eliminating various types of uncertainty that exist in managing of power facilities by means of intelligent automated process control systems at the key stages of the power equipment life cycle are described.

Analyzing the Performance of Web-services during Traffic Anomalies

Intentional or unintentional, service denial leads to substantial economic and reputation losses to the users and the web-service provider. However, it is possible to take proper measures only if we understand and quantify the impact of such anomalies on the victim. In this paper, essential performance metrics distinguishing both transmission issues and application issues have been discussed and evaluated. The legitimate and attack traffic has been synthetically generated in hybrid testbed using open-source software tools. The experiment covers two scenarios, representing DDoS attacks and Flash Events, with varying attack strengths to analyze the impact of anomalies on the server and the network. It has been demonstrated that as the traffic surges, response time increases, and the performance of the target web-server degrades. The performance of the server and the network is measured using various network level, application level, and aggregate level metrics, including throughput, average response time, number of legitimate active connections and percentage of failed transactions.

Reproductive Issues in Müllerian Anomalies

Abstract Mullerian duct anomalies represent a heterogenous group of congenital malformations resulting from abnormal formation or incomplete fusion of Mullerian ducts. Most cases present at puberty or early adulthood with primary amenorrhea or fertility problems although many are asymptomatic and remain undiagnosed. The reported frequency of these anomalies varies, consequent mainly to the inaccuracy of diagnostic methods used and the lack of a uniform classification system. The actual reproductive impact of each anomaly continues to remain ill defined. In addition, radiologic and endoscopic advances appear to have increased the number of unnecessary surgical corrections for these defects. This article attempts to review the reproductive impact of various Mullerian anomalies in a simplified manner. (J GYNECOL SURG 28:127)

Advances in research on the neurological and neuropsychiatric phenotype of Klinefelter syndrome

Klinefelter syndrome, 47,XXY is the most common chromosomal aberration among men. It represents a naturally occurring human model for studies of both X-chromosome gene expression and potential androgen effects on brain development and function. The aim of this review is to combine available brain imaging and behavioral data to provide an overview of what we have learned about the neural underpinnings of cognitive, emotional and behavioral dysunctions in Klinefelter syndrome. The behavioral phenotype of 47,XXY is characterized by language, executive and psychomotor dysfunction, as well as socioemotional impairment. The prevalence of schizophrenia, attention deficit hyperactivity disorder, autism spectrum disorders and affective regulation problems is increased. Neuroimaging studies of children and adults with Klinefelter syndrome syndrome show characteristic structural changes from typical individuals. There are increases in the grey matter volume of the sensorimotor and parietooccipital regions, as well as significant reductions in amygdala, hippocampal, insular, temporal and inferior-frontal grey matter volumes. Widespread white matter abnormalities have been revealed, with reductions in some areas (including anterior cingulate, bilaterally) but increases in others (such as left parietal lobe). Mechanisms underlying these developmental anomalies could include imbalance in gene dosage relative to typical men or women, as well as the potential consequence of endocrinological deficits. Studies of Klinefelter syndrome could generate important information about the impact of anomalies in sex chromosome gene regulation on the development of cerebral grey and white matter and, ultimately, on human behavior.

Evolutionary computation as an artificial attacker: generating evasion attacks for detector vulnerability testing

Intrusion detection systems protect our infrastructures by monitoring for signs of intrusions. However, intrusion detection systems are themselves susceptible to vulnerabilities, which the attackers take advantage of to evade detection. In particular, we focus on evasion attacks in which the attacker aims to generate a stealthy attack that eliminates or minimizes the likelihood of detection. Attackers achieve stealth by mimicking normal behaviour while achieving the attack goals, hence bypassing the detector. Previous work focused on generating evasion attacks using the internal knowledge of the detectors, hence adopting a ‘white-box’ access to the detector. On the other hand, we adopt a ‘black-box’ approach and propose an evolutionary attacker based on Genetic Programming. The access of our ‘black-box’ approach is limited to the feedback of the detector such as anomaly rates and delays. We compare our ‘black-box’ approach with various ‘white-box’ approaches to investigate its effectiveness. In doing so, the impact of anomalies from the break-in stage of the attacks and the delays based on locality frame counts are also discussed. This is particularly important if the performance comparison is to reflect the real capabilities of detectors.

Considerations of data handling system of a spaceborne imaging spectrometer with onboard data compression

This paper outlines the data handling system (DHS) of a spaceborne hyperspectral imager with an onboard data compressor. The data compression techniques to be used are successive approximation multistage vector quantization and hierarchical self-organizing cluster vector quantization. Considerations and implementation aspects of the DHS related to the onboard data compression are addressed. The impact of anomalies (spikes, saturation, etc.) in the raw data on compression performance is evaluated for the purpose of determining whether or not onboard data scrubbing is required before compression. The evaluation results show that anomalies in raw data have no significant effect on compression. This paper evaluates the impact of preprocessing and the conversion of raw data to radiance units on data compression using remote sensing applications to examine whether or not they should be applied on board before compression. The evaluation results show that preprocessing and radiometric conversion applied either before or after compression have no impact on an application using leaf area index but have impact on a target detection application using spectral unmixing. This paper also examines the combination of the two compression techniques to see if there is a performance improvement over a single technique. The experimental results show that the combined compression system does not perform better than either technique alone. Lastly, the resilience of the two compression techniques against bit errors caused by single event upsets is examined. The experimental results show that there is no loss of data fidelity when the error rate is ≤10–6.

Portfolio performance with controlled variables in the Singapore Stock Market

Recently, many stock return ‘anomalies’ have been identified. This leads to increasing agreement among researchers that these anomalies are relevant to enhancing investment performance. This study examines the existence of stock return anomalies on the Stock Exchange of Singapore in the period 1979–87, using a behavioural approach. The results reveal that the impact of anomalies on stock returns is unstable, with the direction of the impact determined by the strength of the stock market. This phenomenon and other observed anomalies may be partially explained by investors' psychology.